{"id":775189,"date":"2021-09-15T14:03:16","date_gmt":"2021-09-15T21:03:16","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=775189"},"modified":"2021-09-15T14:03:34","modified_gmt":"2021-09-15T21:03:34","slug":"vibrational-modes-on-the-non-linear-motion-of-an-oscillating-buble-in-a-newtonian-fluid-using-neural-networks","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/vibrational-modes-on-the-non-linear-motion-of-an-oscillating-buble-in-a-newtonian-fluid-using-neural-networks\/","title":{"rendered":"Vibrational modes on the non linear motion of an oscillating buble in a Newtonian fluid using Neural Networks"},"content":{"rendered":"<p>In this paper we explore the radial motion of a spherical bubble oscillating immersed in a Newtonian fluid due<br \/>\nto an harmonic acoustic pressure forcing. The ordinary differential Rayleigh-Plesset equation governs the non-linear<br \/>\nmotion of the bubble. Several non-linear responses of the bubble motion are explored and we discuss the in details<br \/>\nhow the main physical parameters, expressed in terms of the Reynolds and Weber numbers, influence the non-linear<br \/>\nmotion of this particular dynamic system. The methodology used to explore the problem is based on the control of the<br \/>\nnon-linear dynamic system. We also provide an asymptotic theory to predict how the buble radius vary with time for<br \/>\nsmall amplitudes of the forcing presure field. Different vibrational modes of the bubble motion are examined for several<br \/>\nvalues of the dynamical physical parameters Reynolds and Weber numbers. In addition, this paper presents, based on<br \/>\nfeed-forward backpropagation neural network theory, a method aiming to perform an identification of the vibrational<br \/>\npattern minimizing the error for further pratical applications. Neural networks are computational models inspired by an<br \/>\nanimal\u2019s central nervous systems which is capable of pattern recognition in this case. A learning algorithm is produced<br \/>\nbased on information given and different wheights are adjusted &#8211; simulating neurons &#8211; becoming able to approximate<br \/>\nnon-linear functions. The method used, backpropagation, is a supervised learning method, and is a generalization of the<br \/>\ndelta rule. It requires a dataset of the desired output for many inputs, making up the training set. In the context of this<br \/>\nwork, the training have been composed of different values of Reynolds and Weber numbers, amplitude of the excitation<br \/>\npressure and vibrational pattern. Under these conditions, four different vibrational patterns were identified. The results<br \/>\nof this works may lead to different combination of neuron numbers, trainning epochs and activation transfer function<br \/>\nthat can create a very good recognition method.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we explore the radial motion of a spherical bubble oscillating immersed in a Newtonian fluid due to an harmonic acoustic pressure forcing. The ordinary differential Rayleigh-Plesset equation governs the non-linear motion of the bubble. Several non-linear responses of the bubble motion are explored and we discuss the in details how the main [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"XVII International Symposium on Dynamic Problems of 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